8e01ae829b
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/41916 PiperOrigin-RevId: 326343156 Change-Id: I9810a3301570bf5a25e97b3004fe0043f8ee01db
248 lines
9.2 KiB
Python
248 lines
9.2 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for reduction operators."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import functools
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import itertools
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors_impl
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import googletest
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@parameterized.named_parameters(('32_bit_index', dtypes.int32),
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('64_bit_index', dtypes.int64))
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class ReduceOpsTest(xla_test.XLATestCase, parameterized.TestCase):
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def _testReduction(self,
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tf_reduce_fn,
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np_reduce_fn,
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dtype,
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test_inputs,
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index_dtype,
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rtol=1e-4,
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atol=1e-4):
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"""Tests that the output of 'tf_reduce_fn' matches numpy's output."""
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for test_input in test_inputs:
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with self.session() as sess:
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with self.test_scope():
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a = array_ops.placeholder(dtype)
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index = array_ops.placeholder(index_dtype)
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out = tf_reduce_fn(a, index)
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result = sess.run(out, {a: test_input, index: [0]})
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self.assertAllClose(
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result, np_reduce_fn(test_input, axis=0), rtol=rtol, atol=atol)
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result = sess.run(out, {a: test_input, index: [1]})
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self.assertAllClose(
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result, np_reduce_fn(test_input, axis=1), rtol=rtol, atol=atol)
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result = sess.run(out, {a: test_input, index: [-1]})
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self.assertAllClose(
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result, np_reduce_fn(test_input, axis=1), rtol=rtol, atol=atol)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError, 'Invalid reduction dim'):
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sess.run(out, {a: test_input, index: [-33]})
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError, 'Invalid reduction dim'):
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sess.run(out, {a: test_input, index: [2]})
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REAL_DATA = [
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np.zeros(shape=(2, 0)),
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np.zeros(shape=(0, 30)),
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np.arange(1, 7).reshape(2, 3),
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np.arange(-10, -4).reshape(2, 3),
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np.arange(-4, 2).reshape(2, 3),
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]
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COMPLEX_DATA = [
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np.zeros(shape=(2, 0)).astype(np.complex64),
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np.zeros(shape=(0, 30)).astype(np.complex64),
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np.arange(1, 13, dtype=np.float32).view(np.complex64).reshape(2, 3),
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np.arange(-14, -2, dtype=np.float32).view(np.complex64).reshape(2, 3),
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np.arange(-4, 8, dtype=np.float32).view(np.complex64).reshape(2, 3),
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]
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NONEMPTY_REAL_DATA = [x for x in REAL_DATA if np.size(x) > 0]
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NONEMPTY_COMPLEX_DATA = [x for x in COMPLEX_DATA if np.size(x) > 0]
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BOOL_DATA = [
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np.array([], dtype=np.bool).reshape(2, 0),
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np.array([], dtype=np.bool).reshape(0, 3),
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np.array([[False, True, False], [True, True, False]]),
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]
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ONES = [np.ones([34000, 2])]
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def testReduceSumF32(self, index_dtype):
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self._testReduction(math_ops.reduce_sum, np.sum, np.float32, self.REAL_DATA,
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index_dtype)
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def testReduceSumC64(self, index_dtype):
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self._testReduction(math_ops.reduce_sum, np.sum, np.complex64,
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self.COMPLEX_DATA, index_dtype)
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def testReduceProdF32(self, index_dtype):
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self._testReduction(math_ops.reduce_prod, np.prod, np.float32,
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self.REAL_DATA, index_dtype)
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def testReduceProdC64(self, index_dtype):
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self._testReduction(math_ops.reduce_prod, np.prod, np.complex64,
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self.COMPLEX_DATA, index_dtype)
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def testReduceMin(self, index_dtype):
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def reference_min(dtype, inp, axis):
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"""Wrapper around np.amin that returns +infinity for an empty input."""
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if inp.shape[axis] == 0:
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if np.issubdtype(dtype, np.floating):
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return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('inf'))
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return np.full(inp.shape[0:axis] + inp.shape[axis + 1:],
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np.iinfo(dtype).max)
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return np.amin(inp, axis)
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for dtype in set(self.all_types).intersection(
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[np.float32, np.int32, np.int64]):
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self._testReduction(math_ops.reduce_min,
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functools.partial(reference_min, dtype), dtype,
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self.REAL_DATA, index_dtype)
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def testReduceMax(self, index_dtype):
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def reference_max(dtype, inp, axis):
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"""Wrapper around np.amax that returns -infinity for an empty input."""
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if inp.shape[axis] == 0:
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if np.issubdtype(dtype, np.floating):
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return np.full(inp.shape[0:axis] + inp.shape[axis + 1:],
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float('-inf'))
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return np.full(inp.shape[0:axis] + inp.shape[axis + 1:],
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np.iinfo(dtype).min)
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return np.amax(inp, axis)
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for dtype in set(self.all_types).intersection(
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[np.float32, np.int32, np.int64]):
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self._testReduction(math_ops.reduce_max,
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functools.partial(reference_max, dtype), dtype,
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self.REAL_DATA, index_dtype)
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def testReduceMeanF32(self, index_dtype):
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# TODO(phawkins): mean on XLA currently returns 0 instead of NaN when
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# reducing across zero inputs.
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self._testReduction(math_ops.reduce_mean, np.mean, np.float32,
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self.NONEMPTY_REAL_DATA, index_dtype)
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def testReduceMeanF16(self, index_dtype):
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if np.float16 in self.all_types:
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self._testReduction(math_ops.reduce_mean, np.mean, np.float16, self.ONES,
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index_dtype)
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def testReduceMeanC64(self, index_dtype):
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self._testReduction(math_ops.reduce_mean, np.mean, np.complex64,
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self.NONEMPTY_COMPLEX_DATA, index_dtype)
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def testReduceAll(self, index_dtype):
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self._testReduction(math_ops.reduce_all, np.all, np.bool, self.BOOL_DATA,
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index_dtype)
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def testReduceAny(self, index_dtype):
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self._testReduction(math_ops.reduce_any, np.any, np.bool, self.BOOL_DATA,
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index_dtype)
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def testReduceSumWithDuplicateAxes(self, index_dtype):
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with self.session() as sess:
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with self.test_scope():
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a = array_ops.placeholder(np.float32)
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index = array_ops.placeholder(np.int32)
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out = math_ops.reduce_sum(a, index)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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'Axes contains duplicate dimension'):
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sess.run(out, {a: [10, 20, 30], index: [0, 0]})
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class ReduceOpPrecisionTest(xla_test.XLATestCase):
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def _testReduceSum(self,
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expected_result,
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dtype,
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test_inputs,
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rtol=1e-3,
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atol=1e-4):
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"""Tests reduce sum on a list of input arrays.
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For each array in test_inputs, check that performing reduce sum on the array
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produces a value that is close to the expected result.
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Args:
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expected_result: the expected result.
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dtype: the data type of the reduce sum operation.
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test_inputs: a list of input arrays for the reduce sum operation.
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rtol: the relative error.
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atol: the absolute error.
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"""
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for test_input in test_inputs:
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with self.session() as sess:
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with self.test_scope():
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a = array_ops.placeholder(dtype)
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index = array_ops.placeholder(dtypes.int32)
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out = math_ops.reduce_sum(a, index)
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result = sess.run(out, {
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a: np.array(test_input, dtype=dtype),
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index: [0]
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})
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# Compare the results using float32 type.
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self.assertAllClose(
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np.float32(result),
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np.float32(expected_result),
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rtol=rtol,
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atol=atol)
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def testReduceSumF16(self):
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"""Tests the reduce sum of float16 doesn't lose too much precision."""
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if np.float16 not in self.all_types:
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return
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f16_max = np.finfo(np.float16).max
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self._testReduceSum(
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f16_max, np.float16,
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itertools.permutations([f16_max, f16_max, f16_max * (-1.0)], 3))
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def testReduceSumBF16(self):
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"""Tests the reduce sum of bfloat16 doesn't lose too much precision."""
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if dtypes.bfloat16.as_numpy_dtype not in self.all_types:
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return
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bf16_max = np.float32(dtypes.bfloat16.max)
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f32_max = dtypes.float32.max
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value = min(bf16_max, f32_max - bf16_max) / 2
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self._testReduceSum(
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dtypes.bfloat16.as_numpy_dtype(value), dtypes.bfloat16.as_numpy_dtype,
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itertools.permutations([bf16_max, value, bf16_max * (-1.0)], 3))
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if __name__ == '__main__':
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googletest.main()
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